Currently, audience analytics are carried out by a few large companies across several different media, most notably television (TV) audience measurement carried out by Nielsen Media Research. The data is collected in several different ways, including electronic devices attached to televisions (people meters) that require each member of the household (and visitors) to enter in codes corresponding to themselves, at each instance when a particular viewer watches a program. Additionally, this conventional method can require paper diaries that viewers keep listing all of the programs viewed. In all cases, the people participating are selected in a controlled manner that attempts, by sampling methods, to be representative of the TV viewing public as a whole.
The main drawback to this approach is the expense associated with selecting and maintaining a sample size large enough to yield statistically significant results. For example, as cable and satellite options have expanded the number of available choices for the viewer, the audience for each station has become further fragmented to the point where the existing sample sizes used in audience analytics become too small to produce statistically significant results for these stations. The decreasing audience sample sizes also make it difficult for advertisers to clearly determine what kind of audience certain stations possess. Likewise, cable operators, satellite system providers, and multiple system operators (MSOs) have no precise way of knowing which stations to add or drop from their lineup or how best to package stations into service tiers.
The limitations of sample size can be overcome by choosing a much larger sample. However, to avoid the cost of creating a very large representative sample, a sampling service must select people at random in a cost effective manner, requiring an inexpensive data collection mechanism that includes low-cost incentives for people to join a sampling panel. One company, ComScore, has attempted this on the Internet by constructing a large panel of over a million users. ComScore generates panels by giving people incentives to download the tracking software ComScore uses to monitor their Internet Web surfing activities. However, critics point out that panels constructed employing methods such as ComScore's are statistically un-useful because only certain types of people would give up their privacy in exchange for money or free software. Also, the reliability of data provided by services such as ComScore is suspect, since they must rely on users to provide accurate information when joining. For example, each new user provides demographic information by completing a survey, but there is no accurate method of verifying that users told the truth.
To date, legal and technical issues have impeded accurate measurement of television audience demographic information. For example, regulations set strict limits on how a MSO can utilize personally identifiable information, thereby preventing a MSO to easily or cost-effectively obtain the required consent from the millions of people needed to create a useful sample group. It would be desirable to create passive methods and systems for generating audience analytics by analyzing user identifiable information. It would also be desirable to provide a system that creates analytics anonymously, whereby the analytics can be sold to others, used to deliver targeted content or advertising, or used for other additional purposes.